Standard meta-training overlooks many challenges stemming from real-world dynamics, such as class-imbalance (CI). The standard setting assumes that all classes in the support set contain the same number of data points, whereas in many practical applications, the number of samples for each class may vary [1]}, [2]}. Given the limited amount of data used in FSL, a small difference in the number of samples between classes could already introduce significant levels of imbalance. Most FSL methods are not designed to cope with these more challenging settings. Figure REF  exemplifies these considerations by showing that several state-of-the-art FSL methods underperform when tested under three CI regimes (linear, step, random).
